Below are descriptive and correlational statistics with data from to the International Civic and Citizenship Education Study (ICCS) 2009 and 2016, in which six (Chile, Mexico, Colombia, Paraguay, Guatemala, and the Dominican Republic) and five (Chile, Mexico, Colombia, Peru, and the Dominican Republic) Latin American countries participated, respectively. The following sections are presented: change in support for dictatorship, support for Dictatorship on civic knowledge and support for Dictatorship on institutional trust

Data Frames

  • ICCS 2009: Chile, Mexico, Colombia, Paraguay, Guatemala, and the Dominican Republic

  • ICCS 2016: Chile, Mexico, Colombia, Peru, and the Dominican Republic

Sample

  • All observations
#Data frames with all variables
#2009
dict09 = iccs09 %>%
  mutate(time          = 2009) %>%
  mutate(country       = COUNTRY) %>%
  mutate(idcountry     = IDCNTRY) %>%
  mutate(idschool       = IDSCHOOL) %>%
  mutate(idstudent     = IDSTUD) %>%
  mutate(idstudent = (time*1000000000000 + idcountry*100000000 + idstudent)) %>% #NEW IDSTUDENT
  mutate(dict_safety   = 5-LS2P03D) %>% #DICTATORSHIPS ARE JUSTIFIED WHEN THEY BRING ORDER AND SAFETY
  mutate(dict_benefits = 5-LS2P03E) %>% #DICTATORSHIPS ARE JUSTIFIED WHEN THEY BRING ECONOMIC BENEFITS
  mutate(dict          = (dict_safety + dict_benefits)/2) %>% #MEAN DIC
  mutate(pv1civ        = PV1CIV) %>%
  mutate(pv2civ        = PV2CIV) %>%
  mutate(pv3civ        = PV3CIV) %>%
  mutate(pv4civ        = PV4CIV) %>%
  mutate(pv5civ        = PV5CIV) %>%
  mutate(civic_knowledge = (pv1civ + pv2civ + pv3civ + pv4civ + pv5civ)/5) %>% #MEAN CIVIC KNOWLEDGE 
  mutate(s_intrust       = INTRUST) %>% #TRUST IN CIVIC INSTITUTIONS 
  mutate(s_opdisc        = OPDISC) %>%  #OPENNESS IN CLASS DISCUSSION
  mutate(s_hisced        = HISCED) %>%  #HIGHEST PARENTAL EDUCATIONAL LEVEL 
  mutate(univ            = ifelse(s_hisced>3, 1, 0)) %>% #UNIVERSITARY PARENTS
  mutate(s_hisei         = HISEI) %>%   #PARENT'S HIGHEST OCCUPATIONAL STATUS
  mutate(s_homelit       = HOMELIT) %>% #HOME LITERACY
  mutate(s_gender        = SGENDER) %>% #GENDER OF STUDENT
  mutate(s_citcon        = CITCON) %>%  #CONVENTIONAL CITIZENSHIP
  mutate(s_citsoc        = CITSOC) %>%  #SOCIAL MOVEMENT REL. CITIZENSHIP
  mutate(s_citeff        = CITEFF) %>%  #CITIZENSHIP SELF-EFFICACY 
  mutate(s_cntatt        = ATTCNT) %>%  #ATTITUDES TOWARDS OWN COUNTRY 
  mutate(s_geneql        = GENEQL) %>%  #ATTITUDES TOWARDS GENDER EQUALITY
  mutate(s_ethrght       = ETHRGHT) %>% #EQUAL RIGHTS FOR ALL ETHNIC GROUPS 
  mutate(l_attviol       = ATTVIOL) %>% #ATTITUDES: USE OF VIOLENCE 
  mutate(l_attdiv        = ATTDIFF) %>% #ATTITUDES: NEIGHBOURHOOD DIVERSITY
  mutate(l_autgov        = AUTGOV) %>%  #AUTHORITARIANISM IN GOVERNMENT 
  mutate(l_attcorr       = ATTCORR) %>% #CORRUPT PRACTICES IN GOVERNMENT
  mutate(l_dislaw        = DISLAW) %>%  #ATTITUDES: DISOBEYING THE LAW
  mutate(l_empclas       = EMPATH) %>%  #EMPATHY TOWARDS CLASSMATES
  mutate(s_poldisc       = POLDISC) %>% #DISCUSSION OF POL. AND SOC. ISSUES
  #TRUST
  mutate(nac_gob         = IS2P27A) %>% #TRUST INSTITUTIONS-NATIONAL GOVERNMENT  
  mutate(local_gob     = IS2P27B) %>% #TRUST INSTITUTIONS-LOCAL GOVERNMENT 
  mutate(courts        = IS2P27C) %>% #TRUST INSTITUTIONS-COURTS
  mutate(police        = IS2P27D) %>% #TRUST INSTITUTIONS-POLICE 
  mutate(pol_parties   = IS2P27E) %>% #TRUST INSTITUTIONS-POLITICAL PARTIES
  mutate(parliament    = IS2P27F) %>% #TRUST INSTITUTIONS-PARLIAMENT
  mutate(media         = IS2P27G) %>% #TRUST INSTITUTIONS-MEDIA
  mutate(ffaa          = IS2P27H) %>% #TRUST INSTITUTIONS-FFAA 
  mutate(school        = IS2P27I) %>% #TRUST INSTITUTIONS-SCHOOL
  mutate(unit_nations  = IS2P27J) %>% #TRUST INSTITUTIONS-UNITED NATIONS 
  mutate(people        = IS2P27K) %>% #TRUST INSTITUTIONS-PEOPLE
  #WEITHINGS
  mutate(totwgts       = TOTWGTS) %>%   #FINAL STUDENT WEIGHT
  mutate(wgtfac1       = WGTFAC1) %>%   #SCHOOL BASE WEIGHT
  mutate(wgtadj1s      = WGTADJ1S) %>%  #SCHOOL WEIGHT ADJUSTMENT-STUDENT STUDY
  mutate(wgtfac2s      = WGTFAC2S) %>%  #CLASS WEIGHT FACTOR
  mutate(wgtadj2s      = WGTADJ2S) %>%  #CLASS WEIGHT ADJUSTMENT
  mutate(wgtadj3s      = WGTADJ3S) %>%  #STUDENT WEIGHT ADJUSTMENT
  mutate(jkzones       = JKZONES) %>%   #JACKKNIFE ZONE - STUDENT STUDY
  mutate(jkreps        = JKREPS)  %>%  #JACKKNIFE REPLICATE CODE
  select(411:463)
  
#2016  
dict16 = iccs16 %>%
  mutate(time            = 2016) %>%
  mutate(country         = COUNTRY) %>%
  mutate(idcountry       = IDCNTRY) %>%
  mutate(idschool        = IDSCHOOL) %>%
  mutate(idstudent       = IDSTUD) %>%
  mutate(idstudent       = (time*100000000000 + idcountry*100000000 + idstudent)) %>% #NEW IDSTUDENT
  mutate(dict_safety     = 5-LS3G02D) %>% #DICTATORSHIPS ARE JUSTIFIED WHEN THEY BRING ORDER AND SAFETY
  mutate(dict_benefits   = 5-LS3G02E) %>% #DICTATORSHIPS ARE JUSTIFIED WHEN THEY BRING ECONOMIC BENEFITS
  mutate(dict            = (dict_safety + dict_benefits)/2) %>% #MEAN DIC
  mutate(pv1civ          = PV1CIV) %>%
  mutate(pv2civ          = PV2CIV) %>%
  mutate(pv3civ          = PV3CIV) %>%
  mutate(pv4civ          = PV4CIV) %>%
  mutate(pv5civ          = PV5CIV) %>%
  mutate(civic_knowledge = (pv1civ + pv2civ + pv3civ + pv4civ + pv5civ)/5) %>% #MEAN CIVIC KNOWLEDGE 
  mutate(s_intrust       = S_INTRUST) %>% #TRUST IN CIVIC INSTITUTIONS 
  mutate(s_opdisc        = S_OPDISC) %>%  #OPENNESS IN CLASS DISCUSSION
  mutate(s_hisced        = S_HISCED) %>%  #HIGHEST PARENTAL EDUCATIONAL LEVEL 
  mutate(univ            = ifelse(s_hisced>3, 1, 0)) %>% #UNIVERSITARY PARENTS
  mutate(s_hisei         = S_HISEI) %>%   #PARENT'S HIGHEST OCCUPATIONAL STATUS
  mutate(s_homelit       = S_HOMLIT) %>%  #HOME LITERACY
  mutate(s_gender        = S_GENDER) %>%  #GENDER OF STUDENT
  mutate(s_citcon        = S_CITCON) %>%  #CONVENTIONAL CITIZENSHIP
  mutate(s_citsoc        = S_CITSOC) %>%  #SOCIAL MOVEMENT REL. CITIZENSHIP
  mutate(s_citeff        = S_CITEFF) %>%  #CITIZENSHIP SELF-EFFICACY 
  mutate(s_cntatt        = S_CNTATT) %>%  #ATTITUDES TOWARDS OWN COUNTRY 
  mutate(s_geneql        = S_GENEQL) %>%  #ATTITUDES TOWARDS GENDER EQUALITY
  mutate(s_ethrght       = S_ETHRGHT) %>% #EQUAL RIGHTS FOR ALL ETHNIC GROUPS 
  mutate(l_attviol       = L_ATTVIOL) %>% #ATTITUDES: USE OF VIOLENCE 
  mutate(l_attdiv        = L_ATTDIV) %>%  #ATTITUDES: NEIGHBOURHOOD DIVERSITY
  mutate(l_autgov        = L_AUTGOV) %>%  #AUTHORITARIANISM IN GOVERNMENT 
  mutate(l_attcorr       = L_ATTCORR) %>% #CORRUPT PRACTICES IN GOVERNMENT
  mutate(l_dislaw        = L_DISLAW) %>%  #ATTITUDES: DISOBEYING THE LAW
  mutate(l_empclas       = L_EMPCLAS) %>% #EMPATHY TOWARDS CLASSMATES
  mutate(s_poldisc       = S_POLDISC) %>% #DISCUSSION OF POL. AND SOC. ISSUES  
  #TRUST
  mutate(nac_gob         = IS3G26A) %>%   #TRUST INSTITUTIONS-NATIONAL GOVERNMENT 
  mutate(local_gob       = IS3G26B) %>%   #TRUST INSTITUTIONS-LOCAL GOVERNMENT 
  mutate(courts          = IS3G26C) %>%   #TRUST INSTITUTIONS-COURTS
  mutate(police          = IS3G26D) %>%   #TRUST INSTITUTIONS-POLICE
  mutate(pol_parties     = IS3G26E) %>%   #TRUST INSTITUTIONS-POLITICAL PARTIES
  mutate(parliament      = IS3G26F) %>%   #TRUST INSTITUTIONS-PARLIAMENT
  mutate(media           = IS3G26G) %>%   #TRUST INSTITUTIONS-MEDIA
  mutate(ffaa            = IS3G26I) %>%   #TRUST INSTITUTIONS-FFAA
  mutate(school          = IS3G26J) %>%   #TRUST INSTITUTIONS-SCHOOL
  mutate(unit_nations    = IS3G26K) %>%   #TRUST INSTITUTIONS-UNITED NATIONS
  mutate(people          = IS3G26L) %>%   #TRUST INSTITUTIONS-PEOPLE
  #WEITHINGS
  mutate(totwgts         = TOTWGTS) %>%   #FINAL STUDENT WEIGHT
  mutate(wgtfac1         = WGTFAC1) %>%   #SCHOOL BASE WEIGHT
  mutate(wgtadj1s        = WGTADJ1S) %>%  #SCHOOL WEIGHT ADJUSTMENT-STUDENT STUDY
  mutate(wgtfac2s        = WGTFAC2S) %>%  #CLASS WEIGHT FACTOR
  mutate(wgtadj2s        = WGTADJ2S) %>%  #CLASS WEIGHT ADJUSTMENT
  mutate(wgtadj3s        = WGTADJ3S) %>%  #STUDENT WEIGHT ADJUSTMENT
  mutate(jkzones         = JKZONES)  %>%  #JACKKNIFE ZONE - STUDENT STUDY
  mutate(jkreps          = JKREPS)  %>%  #JACKKNIFE REPLICATE CODE
  select(519:571)

#Merge data
dict <- rbind(dict09, dict16)
#All observations with complete row data
dict2 <- dict[complete.cases(dict),]
#Number observations
dict_count <- dict %>% 
  group_by(time, country) %>%
  dplyr::summarise(N=n()) %>%
  mutate(Prop. = N / sum(N))  %>%
  arrange(country)
#Table
kable(dict_count, align = c("cccc"), 
      col.names = c("Year","Country", "Prop.", "N"),
      caption = "Sample with all observations", format="html") %>%
  kable_styling(bootstrap_options = c("striped", "hover"))
time country N Prop.
2009 CHL 5173 0.1730332
2016 CHL 5081 0.2006793
2009 COL 6200 0.2073856
2016 COL 5609 0.2215332
2009 DOM 4569 0.1528298
2016 DOM 3937 0.1554959
2009 GTM 3998 0.1337303
2009 MEX 6565 0.2195946
2016 MEX 5526 0.2182551
2016 PER 5166 0.2040365
2009 PRY 3391 0.1134265
  • Missing Value delete
time country N Prop.
2009 CHL 4511 0.1093337
2016 CHL 4070 0.0986451
2009 COL 4790 0.1160959
2016 COL 4461 0.1081219
2009 DOM 2164 0.0524492
2016 DOM 2359 0.0571754
2009 GTM 2930 0.0710148
2009 MEX 5127 0.1242638
2016 MEX 4530 0.1097942
2016 PER 4169 0.1010446
2009 PRY 2148 0.0520614

Change in support for dictatorship

  • Dictatorships are justified when they bring economic benefits 2016: LS2P03E - LS3G02E

  • Dictatorships are justified when they bring order and safety 2009: LS2P03D - LS3G02D

Support for Dictatorship and civic knowledge

  • Civic knowledge proxy: Mean to PV1CIV, PV2CIV, PV3CIV, PV4CIV, PV5CIV.
country mean sd n se time
CHL 490.2998 91.60244 5081 1.2850867 2016
CHL 494.0536 85.69706 5173 1.1915017 2009
COL 465.8547 77.26525 6200 0.9812696 2009
COL 486.0054 79.18727 5609 1.0573351 2016
DOM 380.9117 61.77524 4569 0.9139109 2009
DOM 382.8483 75.71249 3937 1.2066598 2016
GTM 432.6359 71.50737 3998 1.1309136 2009
MEX 455.7181 78.47208 6565 0.9684959 2009
MEX 469.3019 79.25553 5526 1.0661643 2016
PER 442.6917 87.93071 5166 1.2233856 2016
PRY 428.7423 84.05566 3391 1.4434543 2009

Support for Dictatorship and institutional trust

Latin American 2009-2016: Institutional trust

country mean sd n se time
CHL 47.06615 10.965115 4070 0.1718763 2016
CHL 49.81702 9.650716 4511 0.1436889 2009
COL 48.06496 9.497960 4461 0.1422048 2016
COL 49.35341 10.385844 4790 0.1500631 2009
DOM 54.14559 11.956145 2164 0.2570174 2009
DOM 54.98023 11.327530 2359 0.2332229 2016
GTM 46.91566 9.569341 2930 0.1767861 2009
MEX 48.46713 10.355470 5127 0.1446233 2009
MEX 49.61967 10.893892 4530 0.1618579 2016
PER 47.84578 9.585716 4169 0.1484597 2016
PRY 49.42522 9.333150 2148 0.2013776 2009

Regression Model by country (simple)

#Models Chile
ch09= dict %>% filter(country=="CHL" & time==2009) 
mch09 = lmer(dict ~ s_intrust + civic_knowledge +  (1 | idschool), weights=totwgts, data=ch09)
ch16= dict %>% filter(country=="CHL" & time==2016) 
mch16 = lmer(dict ~ s_intrust + civic_knowledge +  (1 | idschool), weights=totwgts, data=ch16)

#Models Colombia
col09= dict %>% filter(country=="COL" & time==2009) 
mcol09 = lmer(dict ~ s_intrust + civic_knowledge +  (1 | idschool), weights=totwgts, data=col09)
col16= dict %>% filter(country=="COL" & time==2016) 
mcol16 = lmer(dict ~ s_intrust + civic_knowledge +  (1 | idschool), weights=totwgts, data=col16)

#Models República Dominicana
dom09= dict %>% filter(country=="DOM" & time==2009) 
mdom09 = lmer(dict ~ s_intrust + civic_knowledge +  (1 | idschool), weights=totwgts, data=dom09)
dom16= dict %>% filter(country=="DOM" & time==2016) 
mdom16 = lmer(dict ~ s_intrust + civic_knowledge +  (1 | idschool), weights=totwgts, data=dom16)

#Models México
mex09= dict %>% filter(country=="MEX" & time==2009) 
mmex09 = lmer(dict ~ s_intrust + civic_knowledge +  (1 | idschool), weights=totwgts, data=mex09)
mex16= dict %>% filter(country=="MEX" & time==2016) 
mmex16 = lmer(dict ~ s_intrust + civic_knowledge +  (1 | idschool), weights=totwgts, data=mex16)

#Models Guatemala 2009
gtm09= dict %>% filter(country=="GTM" & time==2009) 
mgtm09 = lmer(dict ~ s_intrust + civic_knowledge +  (1 | idschool), weights=totwgts, data=gtm09)

#Models Paraguay 2009
pry09= dict %>% filter(country=="PRY" & time==2009) 
mpry09 = lmer(dict ~ s_intrust + civic_knowledge +  (1 | idschool), weights=totwgts, data=pry09)

#Models Perú 2016
per16= dict %>% filter(country=="PER" & time==2016) 
mper16 = lmer(dict ~ s_intrust + civic_knowledge +  (1 | idschool), weights=totwgts, data=per16)

#Table
library(stargazer)
stargazer(l=list(mch09,mch16,mcol09,mcol16,mdom09, mdom16,mmex09, mmex16), digits = 3, type="html", 
          column.labels=c("Chile 2009", "Chile 2016", "Colombia 2009", "Colombia 2016", "Rep. Dom. 2009", "Rep. Dom. 2016", "Mexico 2009", "Mexico 2016"), 
          covariate.labels =c("Trust institutions", "Civic Knowledge"), 
          dep.var.labels = "Support dictatorship")
Dependent variable:
Support dictatorship
Chile 2009 Chile 2016 Colombia 2009 Colombia 2016 Rep. Dom. 2009 Rep. Dom. 2016 Mexico 2009 Mexico 2016
(1) (2) (3) (4) (5) (6) (7) (8)
Trust institutions 0.014*** 0.013*** 0.009*** 0.011*** 0.011*** 0.012*** 0.010*** 0.016***
(0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)
Civic Knowledge -0.003*** -0.002*** -0.002*** -0.002*** -0.002*** -0.003*** -0.002*** -0.002***
(0.0001) (0.0002) (0.0001) (0.0001) (0.0002) (0.0002) (0.0001) (0.0001)
Constant 3.330*** 3.059*** 3.231*** 3.179*** 3.228*** 3.626*** 3.184*** 2.819***
(0.094) (0.095) (0.078) (0.088) (0.124) (0.118) (0.080) (0.091)
Observations 5,059 4,909 5,733 5,325 3,450 3,395 6,129 5,289
Log Likelihood -5,915.167 -6,382.484 -5,899.622 -5,766.078 -4,168.101 -4,122.095 -6,661.336 -6,419.753
Akaike Inf. Crit. 11,840.330 12,774.970 11,809.240 11,542.160 8,346.201 8,254.190 13,332.670 12,849.510
Bayesian Inf. Crit. 11,872.980 12,807.460 11,842.510 11,575.060 8,376.932 8,284.840 13,366.270 12,882.370
Note: p<0.1; p<0.05; p<0.01
Dependent variable:
Support dictatorship
Guatemala 2009 Paraguay 2016 Perú 2009
(1) (2) (3)
Trust institutions 0.009*** 0.008*** 0.010***
(0.001) (0.001) (0.001)
Civic Knowledge -0.002*** -0.003*** -0.001***
(0.0002) (0.0002) (0.0001)
Constant 3.320*** 3.486*** 2.814***
(0.100) (0.120) (0.082)
Observations 3,745 2,880 4,975
Log Likelihood -3,779.178 -3,237.766 -4,917.179
Akaike Inf. Crit. 7,568.355 6,485.532 9,844.359
Bayesian Inf. Crit. 7,599.496 6,515.360 9,876.919
Note: p<0.1; p<0.05; p<0.01

Multilevel Regression Model (simple)

  • Model to 2009
Dependent variable:
Support dictatorship
Trust institutions 0.010***
(0.0004)
Civic Knowledge -0.002***
(0.0001)
Constant 3.197***
(0.053)
Observations 26,996
Log Likelihood -35,291.470
Akaike Inf. Crit. 70,592.940
Bayesian Inf. Crit. 70,633.950
Note: p<0.1; p<0.05; p<0.01

  • Model to 2016
Dependent variable:
Support dictatorship
Trust institutions 0.015***
(0.0005)
Civic Knowledge -0.002***
(0.0001)
Constant 2.924***
(0.069)
Observations 23,893
Log Likelihood -32,236.060
Akaike Inf. Crit. 64,482.110
Bayesian Inf. Crit. 64,522.520
Note: p<0.1; p<0.05; p<0.01